您好我有一个函数可以对多个列执行计算（使用类型支持表堆栈溢出社区编写）。我想调整它，以便它不是为每次迭代计算MOD，而是在表中查找值并使用它。每列的MOD将不同。
以下是进行计算的函数：
library(data.table)
dataDT<- data.frame(CAG = c(13, 14, 15, 17),
A01 = c(6485,35,132, 12),
A02 = c(0,42,56, 4))
thres <- 0.2
dataDT<-setDT(dataDT)
colsToBeUsed<-names(dataDT[,!'CAG'])
sumDataSetdata<-c()
sumDataSet<-unlist(lapply(X=1:length(colsToBeUsed),function(X){s=colsToBeUsed[X]
eval(parse(text=paste0('dataDT[',s,'<thres*max(',s,'),',s,':=0]')))
eval(parse(text=paste0('dataDT[,MOD',s,':=dataDT[',s,'==max(',s,'),CAG]]')))
eval(parse(text=paste0('dataDT[,norm',s,':=',s,'/sum(',s,')]')))
eval(parse(text=paste0('dataDT[,sum',s,':=',s,'/sum(',s,')*(CAG-MOD',s,'),]')))
eval(parse(text=paste0('rbind(sumDataSetdata,dataDT[,sum(sum',s,')])')))
}))
以下是给出MOD的表格：
MODs <- data.frame(c(data.frame(samples = c('A01', 'A02', 'A03', 'A04'), MOD = c(117.8, 120.2, 124.5, 130.6))
下表列出了每个'样本'列使用哪种MOD
ctrls <- (data.frame(samples = c('A01', 'A02', 'A03', 'A04'), ctrl = c(A01, A01, A03, A03))
对回答1的回应
谢谢，这适用于该示例。我一直在尝试将它应用到我的真实数据中并且遇到一些困难。这是我的真实数据的代码。
library(data.table)
dataDT <- data.frame(area[,7:ncol(height)])
dataDT <- setDT(dataDT)
colsToBeUsed<-names(dataDT[,!'CAG'])
MODs <- data.frame(samples = samples$unique.inputdf.SampleFileName., MOD = htresults$mode)
ctrls <- data.frame(samples = samples$unique.inputdf.SampleFileName., ctrl = 'A01_RR20170609_FA_A01_2017-06-09_1.fsa')
myFun <- function(x, mod, cag, thres) {
x[x < (thres * max(x))] <- 0
norm_x <- x / sum(x)
sum_x <- norm_x * (cag - mod)
sum(sum_x)
}
transision_matrix <- merge(ctrls, MODs, by.x = "ctrl", by.y = "samples")
setDT(transision_matrix)
mf2 <- function(colname, dataDT, transision_matrix){
x <- dataDT[, colname, with = F][[1]]
mod <- transision_matrix[samples == colname, MOD]
cag <- dataDT[, "CAG"][[1]]
myFun(x, mod, cag, thres = 0.2)
}
sapply(colsToBeUsed, function(x) mf2(x, dataDT, transision_matrix))
本实验中所有色谱柱的对照样品为A01_RR20170609_FA_A01_2017.06.09_1.fsa，其模式为20.67000
这是我得到的结果
A01_RR20170609_FA_A01_2017.06.09_1.fsa A02_RR20170609_FA_A02_2017.06.09_1.fsa
0 0
A03_RR20170609_FA_A03_2017.06.09_1.fsa A04_RR20170609_FA_A04_2017.06.09_1.fsa
0 0
A05_RR20170609_FA_A05_2017.06.09_1.fsa A06_RR20170609_FA_A06_2017.06.09_1.fsa
0 0
A07_RR20170609_FA_A07_2017.06.09_1.fsa A08_RR20170609_FA_A08_2017.06.09_1.fsa
0 0
A09_RR20170609_FA_A09_2017.06.09_1.fsa A10_RR20170609_FA_A10_2017.06.09_1.fsa
0 0
A11_RR20170609_FA_A11_2017.06.09_1.fsa A12_RR20170609_FA_A12_2017.06.09_1.fsa
0 0
我期待的结果是：
[1] 4.108246 5.868355 4.608756 -1.159657 4.015066 4.364199 5.262355 4.337760 6.496672 5.574396
[11] 5.102111 8.911440
如果它有用，这里是关于表'height'的信息：
'data.frame': 660 obs. of 19 variables:
$ Dye/SamplePeak : chr "B,66" "B,67" "B,68" "B,69" ...
$ Marker : chr NA NA NA NA ...
$ Allele : chr NA NA NA NA ...
$ Size : num 144 147 148 150 151 ...
$ Area : num 148288 110 907 3355 1274 ...
$ DataPoint : num 2591 2622 2641 2655 2671 ...
$ CAG : num 13.9 14.9 15.5 15.9 16.4 ...
$ A01_RR20170609_FA_A01_2017-06-09_1.fsa: num 6485 32 125 450 211 ...
$ A02_RR20170609_FA_A02_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A03_RR20170609_FA_A03_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A04_RR20170609_FA_A04_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A05_RR20170609_FA_A05_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A06_RR20170609_FA_A06_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A07_RR20170609_FA_A07_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A08_RR20170609_FA_A08_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A09_RR20170609_FA_A09_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A10_RR20170609_FA_A10_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A11_RR20170609_FA_A11_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A12_RR20170609_FA_A12_2017-06-09_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
表'height'非常大，但是我添加了'CAG'和'A01_RR20170609_FA_A01_2017.06.09_1.fsa'列的结果，以防它对工作有用。
**CAG**
[1] 13.92667 14.88000 15.46333 15.89333 16.38333 16.84333 17.39667 17.79667 18.41000 18.78000 19.39333
[12] 19.76000 20.33667 20.67000 21.03333 21.36667 23.52000 24.45667 25.39667 27.31000 28.34667 29.32000
[23] 30.23333 31.17667 32.15000 32.72667 33.08333 33.67667 34.03667 34.60667 34.96667 35.54000 35.93333
[34] 36.54000 36.87333 37.49333 37.87333 38.50667 38.89000 39.48333 39.84667 40.45000 40.81000 41.41000
[45] 41.73667 42.45333 42.77667 43.34000 43.69333 46.21000 49.21333 52.21667 52.60667 13.90000 14.95000
[56] 15.47333 15.99667 16.46000 16.86333 17.48000 17.85000 18.43667 18.80667 19.42667 19.76333 20.43667
[67] 21.38667 21.72333 22.70000 23.31333 23.61667 24.23000 24.59667 25.60333 26.55333 27.16333 27.44000
[78] 28.42000 29.40000 30.37667 30.98667 31.32333 32.30000 33.23667 34.16667 35.13333 36.07667 37.05667
[89] 38.03333 38.63333 39.01667 39.60667 39.97333 40.57667 40.94000 41.54333 41.87333 42.47333 42.83333
[100] 43.46000 43.78667 44.70333 12.48333 13.03667 13.92667 14.88000 15.49333 15.86333 16.44667 16.78333
[111] 17.36667 17.82667 18.38000 18.84333 19.42333 19.79000 20.34000 20.79667 21.28333 21.74333 23.60000
[122] 24.54000 25.51333 26.48667 27.43333 28.40667 29.32000 29.96000 30.26333 30.90333 31.36000 32.24000
[133] 33.17333 34.25667 35.40667 36.01667 36.56667 36.99667 37.58667 37.97000 38.60333 38.98333 39.57667
[144] 39.94000 40.54667 40.90667 41.51000 41.86667 42.43667 42.79333 43.38667 43.74000 12.63667 13.95667
[155] 15.52333 15.92333 16.53667 16.90667 17.49000 17.89000 18.44333 18.81000 19.42333 19.79000 20.40000
[166] 20.76667 21.37667 21.74333 22.38000 24.54000 25.51333 27.46333 28.43667 29.04667 29.38000 30.29333
[177] 31.26667 32.24000 33.17333 34.10667 35.04333 35.68000 35.98667 36.59667 36.96333 37.55667 37.97000
[188] 38.60333 38.95333 39.57667 39.94000 40.51333 40.87333 41.47333 41.83333 42.43000 42.75667 43.35000
[199] 43.70667 44.29667 44.65000 45.24000 45.56000 46.54333 47.54333 12.45333 13.04000 13.87333 14.86667
[210] 15.48667 15.86000 16.44667 16.79000 17.37667 17.81333 18.43333 18.80667 19.42667 19.79667 20.41333
[221] 20.75000 21.36667 21.73333 22.59667 23.54667 24.53000 25.51000 26.46000 27.44000 28.38667 29.00000
[232] 29.33667 29.91667 30.28333 31.26333 31.84333 33.08667 34.08000 34.68667 35.08333 35.97333 36.37333
[243] 36.96000 37.59000 37.94667 38.59000 38.98000 39.57667 39.94333 40.55000 40.88333 41.48667 41.85000
[254] 42.42000 42.77667 43.37333 43.73000 44.29333 44.64667 45.26667 45.56000 12.57667 12.94000 13.55333
[265] 13.92000 14.50000 14.86667 15.41667 15.87667 16.51667 16.88667 17.49667 17.86333 18.47667 18.81333
[276] 19.42333 19.79000 20.40000 20.76667 21.40667 21.74333 23.60000 27.42667 28.94333 29.76333 30.33667
[287] 32.21333 33.17000 34.09333 34.57333 35.05667 35.69000 36.05333 36.69000 36.99667 37.58667 37.97000
[298] 38.60333 38.98333 39.60333 39.96333 40.56333 40.92000 41.48667 41.84333 42.43333 42.78667 43.40667
[309] 43.70000 12.05333 12.51333 12.91333 13.90000 14.48667 14.85667 15.47667 15.78333 16.40000 16.83333
[320] 17.48000 17.88333 18.47000 18.81000 19.39333 19.79000 20.40000 20.76667 21.34667 21.71000 22.26000
[331] 22.62333 23.56667 24.57333 26.43000 27.99333 28.66667 29.40000 31.29333 32.30000 33.17000 34.09000
[342] 34.69000 35.08000 35.65333 36.02000 36.23000 36.93333 37.52667 37.94667 38.59000 38.94667 39.57667
[353] 39.94000 40.54333 40.90333 41.47333 41.83333 42.42667 42.78333 43.40333 43.72667 52.24333 52.57667
[364] 12.02667 12.30000 13.73667 14.99333 15.57333 15.94000 16.52333 16.92000 17.44000 17.96000 18.44667
[375] 18.81333 19.45333 19.81667 20.42000 20.78333 21.35667 21.66000 22.41333 24.52333 25.52000 26.45333
[386] 27.42667 28.06333 28.33333 28.94000 29.36333 29.91000 30.30333 30.88000 31.24333 31.85000 32.18333
[397] 33.14333 33.77000 34.13000 34.70000 35.03000 35.66333 36.02667 36.63333 37.00000 37.58333 37.99000
[408] 38.58333 38.96000 39.57333 39.93333 40.53000 40.88667 41.48000 41.83667 42.43000 42.78333 43.37000
[419] 43.69333 44.36667 44.69000 45.27333 45.65000 46.24000 46.54000 50.86667 51.52667 12.42333 13.03333
[430] 13.85667 14.47000 14.86667 15.47667 15.84667 16.39667 16.70000 17.40667 17.80333 19.21000 19.79000
[441] 20.18667 20.73667 21.31667 21.71000 22.20000 22.59333 23.56667 24.51000 25.48333 26.42667 27.03333
[452] 27.33667 28.33667 29.30667 29.94333 30.91333 31.91000 32.78667 33.64667 34.00333 34.63333 34.99333
[463] 35.59667 35.99333 36.54000 36.93667 37.52333 37.90333 38.54000 38.92000 39.54667 39.90667 40.51000
[474] 40.84000 41.44000 41.79667 42.39333 42.74667 43.31000 43.69333 44.22000 52.21667 52.60667 12.27000
[485] 13.79333 14.95000 15.44000 16.01667 16.44333 16.84000 17.47667 17.84333 18.45000 18.81667 19.42333
[496] 19.69667 20.12000 20.48000 21.38667 21.68667 22.59333 23.52667 24.52333 25.12667 25.45667 26.45667
[507] 26.97333 27.39667 28.33667 28.97333 29.36667 29.91333 30.27667 31.24667 31.82000 32.18333 33.14000
[518] 34.06000 34.65667 35.01667 35.61333 35.97333 36.54667 36.94000 37.52000 37.92667 38.55333 38.92667
[529] 39.51333 39.87000 40.46667 40.82000 41.41333 41.76667 42.35667 42.71000 43.29667 43.64667 44.23000
[540] 44.58000 45.21667 45.53667 46.15000 46.51000 54.90333 12.13000 12.51667 13.81333 14.84000 15.44333
[551] 15.89333 16.74000 17.37333 17.76333 18.33667 18.73000 19.30333 19.60333 20.37667 21.30333 21.63000
[562] 22.52333 23.50667 24.48667 25.46667 26.38667 26.92333 27.34000 27.90667 28.29333 29.24333 30.22667
[573] 30.79333 31.24000 31.83333 32.43000 32.93000 33.46000 34.02333 34.67333 34.97000 35.53667 35.92333
[584] 36.52333 36.88333 37.48667 37.85667 38.50333 38.87333 39.45333 39.83667 40.42667 40.81000 41.36667
[595] 41.71667 42.33000 42.68000 43.26333 43.61000 44.56333 48.30000 12.46000 12.84667 13.83333 14.43000
[606] 14.82000 15.35667 15.65667 16.40333 17.42000 17.78000 18.55667 18.88333 19.30333 19.69000 20.31333
[617] 20.64000 21.23333 21.61667 22.21000 22.47667 23.45667 24.04667 24.40333 24.99333 25.35000 25.94000
[628] 26.29667 26.92000 27.27667 27.84000 28.25667 28.85000 29.20333 30.15333 31.13000 31.72000 32.10667
[639] 32.69667 33.04333 33.91667 34.91000 35.91000 36.44333 36.79667 37.39333 37.79333 38.41000 38.78000
[650] 39.39333 39.77333 40.36000 40.71000 41.29000 41.64000 42.24667 42.59333 43.54667 44.49000 45.40333
A01_RR20170609_FA_A01_2017.06.09_1.fsa
[1] 6485 32 125 450 211 703 553 1549 1360 3526 5028 13610 15986 31233 713 1260
[17] 31 37 33 46 43 48 40 63 78 33 118 40 176 65 296 103
[33] 501 242 923 545 2006 1355 4348 2564 8615 3886 12985 227 669 85 61 57
[49] 103 32 42 50 64 0 0 0 0 0 0 0 0 0 0 0
[65] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[81] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[97] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[273] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[321] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[449] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[497] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[513] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[545] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[657] 0 0 0 0
回答回答2 根据要求进行输入
dput(transision_matrix)
structure(list(ctrl = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = "A01_RR20170609_FA_A01_2017-06-09_1.fsa", class = "factor"),
samples = structure(1:12, .Label = c("A01_RR20170609_FA_A01_2017-06-09_1.fsa",
"A02_RR20170609_FA_A02_2017-06-09_1.fsa", "A03_RR20170609_FA_A03_2017-06-09_1.fsa",
"A04_RR20170609_FA_A04_2017-06-09_1.fsa", "A05_RR20170609_FA_A05_2017-06-09_1.fsa",
"A06_RR20170609_FA_A06_2017-06-09_1.fsa", "A07_RR20170609_FA_A07_2017-06-09_1.fsa",
"A08_RR20170609_FA_A08_2017-06-09_1.fsa", "A09_RR20170609_FA_A09_2017-06-09_1.fsa",
"A10_RR20170609_FA_A10_2017-06-09_1.fsa", "A11_RR20170609_FA_A11_2017-06-09_1.fsa",
"A12_RR20170609_FA_A12_2017-06-09_1.fsa"), class = "factor"),
MOD = c(20.67, 20.67, 20.67, 20.67, 20.67, 20.67, 20.67,
20.67, 20.67, 20.67, 20.67, 20.67)), .Names = c("ctrl", "samples",
"MOD"), row.names = c(NA, -12L), class = c("data.table", "data.frame"
), .internal.selfref = <pointer: 0x10180d178>, index = structure(integer(0), "`__samples`" = integer(0)))
> dput(ctrls)
structure(list(samples = structure(1:12, .Label = c("A01_RR20170609_FA_A01_2017-06-09_1.fsa",
"A02_RR20170609_FA_A02_2017-06-09_1.fsa", "A03_RR20170609_FA_A03_2017-06-09_1.fsa",
"A04_RR20170609_FA_A04_2017-06-09_1.fsa", "A05_RR20170609_FA_A05_2017-06-09_1.fsa",
"A06_RR20170609_FA_A06_2017-06-09_1.fsa", "A07_RR20170609_FA_A07_2017-06-09_1.fsa",
"A08_RR20170609_FA_A08_2017-06-09_1.fsa", "A09_RR20170609_FA_A09_2017-06-09_1.fsa",
"A10_RR20170609_FA_A10_2017-06-09_1.fsa", "A11_RR20170609_FA_A11_2017-06-09_1.fsa",
"A12_RR20170609_FA_A12_2017-06-09_1.fsa"), class = "factor"),
ctrl = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), class = "factor", .Label = "A01_RR20170609_FA_A01_2017-06-09_1.fsa")), .Names = c("samples",
"ctrl"), row.names = c(NA, -12L), class = "data.frame")
> dput(MODs)
structure(list(samples = structure(1:12, .Label = c("A01_RR20170609_FA_A01_2017-06-09_1.fsa",
"A02_RR20170609_FA_A02_2017-06-09_1.fsa", "A03_RR20170609_FA_A03_2017-06-09_1.fsa",
"A04_RR20170609_FA_A04_2017-06-09_1.fsa", "A05_RR20170609_FA_A05_2017-06-09_1.fsa",
"A06_RR20170609_FA_A06_2017-06-09_1.fsa", "A07_RR20170609_FA_A07_2017-06-09_1.fsa",
"A08_RR20170609_FA_A08_2017-06-09_1.fsa", "A09_RR20170609_FA_A09_2017-06-09_1.fsa",
"A10_RR20170609_FA_A10_2017-06-09_1.fsa", "A11_RR20170609_FA_A11_2017-06-09_1.fsa",
"A12_RR20170609_FA_A12_2017-06-09_1.fsa"), class = "factor"),
MOD = c(20.67, 19.7633333333333, 16.7833333333333, 21.7433333333333,
16.79, 14.8666666666667, 15.7833333333333, 21.66, 16.3966666666667,
19.6966666666667, 19.6033333333333, 15.3566666666667)), .Names = c("samples",
"MOD"), row.names = c(NA, -12L), class = "data.frame")
答案 0 :(得分：1)
你的方法看起来很复杂，所以我尝试使用基矢量进行计算。
1）首先，我创建了将向量作为参数的函数，因为提供的evalve代码很难理解。
myFun <- function(x, mod, cag, thres) {
x[x < (thres * max(x))] <- 0
norm_x <- x / sum(x)
sum_x <- norm_x * (cag - mod)
sum(sum_x)
}
我希望我做对了。
2）然后我们创建转换矩阵，我们将从中获取MOD个值。
transision_matrix <- merge(ctrls, MODs, by.x = "ctrl", by.y = "samples")
setDT(transision_matrix)
3）然后我们可以编写以列名和data.table为参数的函数来获得你想要的结果：
mf2 <- function(colname, dataDT, transision_matrix){
x <- dataDT[, colname, with = F][[1]]
mod <- transision_matrix[samples == colname, MOD]
cag <- dataDT[, "CAG"][[1]]
myFun(x, mod, cag, thres = 0.2)
}
4）最后我们只需要通过列名矢量
来应用/提供它sapply(colsToBeUsed, function(x) mf2(x, dataDT, transision_matrix))
A01 A02
-104.8000 -103.2286
问题出现在samples中transision_matrix列的名称中，其中的名称与列名称不匹配。您应该更改dataDT列名称或samples列的值，其格式匹配。
> 'A01_RR20170609_FA_A01_2017-06-09_1.fsa' == "A01_RR20170609_FA_A01_2017.06.09_1.fsa"
[1] FALSE
（改变点） 你可以这样做：
> colnames(dataDT)
[1] "A01_RR20170609_FA_A01_2017-06-09_1.fsa" "CAG"
> colnames(dataDT) <- gsub(".","-", colnames(dataDT), fixed = T) #change all dots to -
> colnames(dataDT) <- gsub("-fsa",".fsa", colnames(dataDT), fixed = T) #change back the end of string
> colnames(dataDT)
[1] "A01_RR20170609_FA_A01_2017-06-09_1.fsa" "CAG"
现在一切都应该有效。
答案 1 :(得分：0)
感谢。这是目前的功能：
library(data.table)
dataDT <- height[,13:ncol(height)] #Create a data frame containing CAG and height columns
dataDT <- setDT(dataDT) #Convert to a data table
colsToBeUsed<-names(dataDT[,!'CAG']) #Assigns the columns to be analysed
myFun <- function(x, mod, cag, thres) { #Function that takes vectors as arguments.
x[x < (thres * max(x))] <- 0 #First sets all heights < 0.2*threshold to 0.
norm_x <- x / sum(x) #Then normalises heights by dividing by the sum of the heights.
sum_x <- norm_x * (cag - mod) #Then multiplies by the change in CAG from mode
sum(sum_x) #Then sums the results
}
transision_matrix <- merge(propsettings, modeHt, by.x = "control", by.y = "sample") #Transition matrix that determines control modes for each sample
setDT(transision_matrix)
mf2 <- function(colname, dataDT, transision_matrix){ #Function that takes column name and data table as arguments.
x <- dataDT[, colname, with = F][[1]]
mod <- transision_matrix[sample == colname, mode] #Vector of CONTROL modes to use
cag <- dataDT[, "CAG"][[1]]
thres <- resultsHt$iithreshold #THIS LIKELY SOURCE OF ERROR AS IT WORKS WITH 0.2
myFun(x, mod, cag, thres)
}
iiHt <- sapply(colsToBeUsed, function(x) mf2(x, dataDT, transision_matrix))
resultsHt$iiHt <- iiHt
这是'resultsHt $ iithreshold'：
[1] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
[29] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
这是modeHt： 样本模式 1 A01_MF20170623_FA_A01_2017-06-23_1.fsa 130.93667 2 A02_MF20170623_FA_A02_2017-06-23_1.fsa 131.14667 3 A03_MF20170623_FA_A03_2017-06-23_1.fsa 132.07333 4 A05_MF20170623_FA_A05_2017-06-23_1.fsa 151.97333 5 A06_MF20170623_FA_A06_2017-06-23_1.fsa 128.39333 6 A07_MF20170623_FA_A07_2017-06-23_1.fsa 116.02333 7 A08_MF20170623_FA_A08_2017-06-23_1.fsa 127.42667 8 A09_MF20170623_FA_A09_2017-06-23_1.fsa 163.22000 9 A10_MF20170623_FA_A10_2017-06-23_1.fsa 131.92667 10 A11_MF20170623_FA_A11_2017-06-23_1.fsa 133.57333 11 A12_MF20170623_FA_A12_2017-06-23_1.fsa 164.85333 12 B01_MF20170623_FA_B01_2017-06-23_1.fsa 180.34333 13 B02_MF20170623_FA_B02_2017-06-23_1.fsa 133.08000 14 B03_MF20170623_FA_B03_2017-06-23_1.fsa 163.53333 15 B04_MF20170623_FA_B04_2017-06-23_1.fsa 133.13333 16 B05_MF20170623_FA_B05_2017-06-23_1.fsa 133.08000 17 B06_MF20170623_FA_B06_2017-06-23_1.fsa 167.23000 18 B07_MF20170623_FA_B07_2017-06-23_1.fsa 115.05667 19 B08_MF20170623_FA_B08_2017-06-23_1.fsa 179.62333 20 C01_MF20170623_FA_C01_2017-06-23_1.fsa 115.93000 21 C02_MF20170623_FA_C02_2017-06-23_1.fsa 115.17333 22 C05_MF20170623_FA_C05_2017-06-23_1.fsa 131.18667 23 C07_MF20170623_FA_C07_2017-06-23_1.fsa 131.13333 24 C08_MF20170623_FA_C08_2017-06-23_1.fsa 131.13000 25 C09_MF20170623_FA_C09_2017-06-23_1.fsa 130.09333 26 C10_MF20170623_FA_C10_2017-06-23_1.fsa 115.09000 27 C11_MF20170623_FA_C11_2017-06-23_1.fsa 130.04000 28 C12_MF20170623_FA_C12_2017-06-23_1.fsa 115.70000 29 D02_MF20170623_FA_D02_2017-06-23_1.fsa 116.03667 30 D03_MF20170623_FA_D03_2017-06-23_1.fsa 131.14000 31 D04_MF20170623_FA_D04_2017-06-23_1.fsa 115.22667 32 D05_MF20170623_FA_D05_2017-06-23_1.fsa 19.88000 33 D06_MF20170623_FA_D06_2017-06-23_1.fsa 19.91000 34 D08_MF20170623_FA_D08_2017-06-23_1.fsa 19.84667 35 D10_MF20170623_FA_D10_2017-06-23_1.fsa 72.32333 36 D11_MF20170623_FA_D11_2017-06-23_1.fsa 130.00333 37 D12_MF20170623_FA_D12_2017-06-23_1.fsa 130.01333 38 A01_MF20170522_FA_A01_2017-05-22_1.fsa 136.94667 39 C02_MF20170529_FA_C02_2017-05-30_1.fsa 132.31667 40 B08_MF20170522_FA_B08_2017-05-22_1.fsa 121.00000
这是'propsettings'：
sample control
1 A01_MF20170623_FA_A01_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
2 A02_MF20170623_FA_A02_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
3 A03_MF20170623_FA_A03_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
4 A05_MF20170623_FA_A05_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
5 A06_MF20170623_FA_A06_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
6 A07_MF20170623_FA_A07_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
7 A08_MF20170623_FA_A08_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
8 A09_MF20170623_FA_A09_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
9 A10_MF20170623_FA_A10_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
10 A11_MF20170623_FA_A11_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
11 A12_MF20170623_FA_A12_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
12 B01_MF20170623_FA_B01_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
13 B02_MF20170623_FA_B02_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
14 B03_MF20170623_FA_B03_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
15 B04_MF20170623_FA_B04_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
16 B05_MF20170623_FA_B05_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
17 B06_MF20170623_FA_B06_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
18 B07_MF20170623_FA_B07_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
19 B08_MF20170623_FA_B08_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
20 C01_MF20170623_FA_C01_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
21 C02_MF20170623_FA_C02_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
22 C05_MF20170623_FA_C05_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
23 C07_MF20170623_FA_C07_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
24 C08_MF20170623_FA_C08_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
25 C09_MF20170623_FA_C09_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
26 C10_MF20170623_FA_C10_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
27 C11_MF20170623_FA_C11_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
28 C12_MF20170623_FA_C12_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
29 D02_MF20170623_FA_D02_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
30 D03_MF20170623_FA_D03_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
31 D04_MF20170623_FA_D04_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
32 D05_MF20170623_FA_D05_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
33 D06_MF20170623_FA_D06_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
34 D08_MF20170623_FA_D08_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
35 D10_MF20170623_FA_D10_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
36 D11_MF20170623_FA_D11_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
37 D12_MF20170623_FA_D12_2017-06-23_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
38 A01_MF20170522_FA_A01_2017-05-22_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
39 C02_MF20170529_FA_C02_2017-05-30_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
40 B08_MF20170522_FA_B08_2017-05-22_1.fsa B08_MF20170522_FA_B08_2017-05-22_1.fsa
这是“身高”的概述：
> str(height)
'data.frame': 1078 obs. of 53 variables:
$ Dye/SamplePeak : chr "B,65" "B,66" "B,67" "B,68" ...
$ Marker : chr NA NA NA NA ...
$ Allele : chr NA NA NA NA ...
$ Size : num 418 432 435 438 441 ...
$ Area : num 285 354 300 334 342 385 359 370 410 439 ...
$ DataPoint : num 5665 5827 5859 5890 5924 ...
$ flank : num 108 108 108 108 108 108 108 108 108 108 ...
$ correction : num 2 2 2 2 2 2 2 2 2 2 ...
$ start : num 100 100 100 100 100 100 100 100 100 100 ...
$ end : num 200 200 200 200 200 200 200 200 200 200 ...
$ control : chr "B08_MF20170522_FA_B08_2017-05-22_1.fsa" "B08_MF20170522_FA_B08_2017-05-22_1.fsa" "B08_MF20170522_FA_B08_2017-05-22_1.fsa" "B08_MF20170522_FA_B08_2017-05-22_1.fsa" ...
$ iithreshold : num 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
$ CAG : num 105 110 111 112 113 ...
$ A01_MF20170623_FA_A01_2017-06-23_1.fsa: num 31 32 32 33 40 37 36 41 45 38 ...
$ A02_MF20170623_FA_A02_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A03_MF20170623_FA_A03_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A05_MF20170623_FA_A05_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A06_MF20170623_FA_A06_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A07_MF20170623_FA_A07_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A08_MF20170623_FA_A08_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A09_MF20170623_FA_A09_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A10_MF20170623_FA_A10_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A11_MF20170623_FA_A11_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A12_MF20170623_FA_A12_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B01_MF20170623_FA_B01_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B02_MF20170623_FA_B02_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B03_MF20170623_FA_B03_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B04_MF20170623_FA_B04_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B05_MF20170623_FA_B05_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B06_MF20170623_FA_B06_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B07_MF20170623_FA_B07_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B08_MF20170623_FA_B08_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C01_MF20170623_FA_C01_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C02_MF20170623_FA_C02_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C05_MF20170623_FA_C05_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C07_MF20170623_FA_C07_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C08_MF20170623_FA_C08_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C09_MF20170623_FA_C09_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C10_MF20170623_FA_C10_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C11_MF20170623_FA_C11_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C12_MF20170623_FA_C12_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D02_MF20170623_FA_D02_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D03_MF20170623_FA_D03_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D04_MF20170623_FA_D04_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D05_MF20170623_FA_D05_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D06_MF20170623_FA_D06_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D08_MF20170623_FA_D08_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D10_MF20170623_FA_D10_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D11_MF20170623_FA_D11_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ D12_MF20170623_FA_D12_2017-06-23_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ A01_MF20170522_FA_A01_2017-05-22_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ C02_MF20170529_FA_C02_2017-05-30_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...
$ B08_MF20170522_FA_B08_2017-05-22_1.fsa: num 0 0 0 0 0 0 0 0 0 0 ...